News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel

News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel
Sign In

Scientists in Italy Develop Hierarchical Artificial Intelligence System to Analyze Bacterial Species in Culture Plates

New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy

Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.

They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.

The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.

The researchers published their findings in the journal Nature titled, “Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology.”

In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.

However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.

“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”

Alberto Signoroni, PhD

“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)

How Hierarchical AI Works

Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”

DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:

  • At level 0, the system determines the number of bacterial colonies and their locations on the plate.
  • At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
  • At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”

The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.

  • At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.

At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.

Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:

  • “Positive” (significant bacterial growth),
  • “No significant growth” (negative), or
  • “Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.

If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.

“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.

Nearly 100% Agreement with Medical Technologists

To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.

Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.

The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.

Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.

Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.

—Stephen Beale

Related Information:

Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology

Hierarchical Deep Learning Neural Network (HiDeNN): An Artificial Intelligence (AI) Framework for Computational Science and Engineering

An AI System Helps Microbiologists Identify Bacteria

This AI Research Helps Microbiologists to Identify Bacteria

Deep Learning Meets Clinical Microbiology: Unveiling DeepColony for Automated Culture Plates Interpretation

University Hospitals Birmingham Claims Its New AI Model Detects Certain Skin Cancers with Nearly 100% Accuracy

But dermatologists and other cancer doctors still say AI is not ready to operate without oversight by clinical physicians

Dermatopathologists and the anatomic pathology profession in general have a new example of how artificial intelligence’s (AI’s) ability to detect cancer with accuracy comparable to a trained pathologist has greatly improved. At the latest European Academy of Dermatology and Venereology (EADV) Congress, scientists presented a study in which researchers with the University Hospitals Birmingham NHS Foundation Trust used an AI platform to assess 22,356 people over 2.5 years.

According to an EADV press release, the AI software demonstrated a “100% (59/59 cases identified) sensitivity for detecting melanoma—the most serious form of skin cancer.” The AI software also “correctly detected 99.5% (189/190) of all skin cancers and 92.5% (541/585) of pre-cancerous lesions.”  

“Of the basal cell carcinoma cases, a single case was missed out of 190, which was later identified at a second read by a dermatologist ‘safety net.’ This further demonstrates the need to have appropriate clinical oversight of the AI,” the press release noted.

AI is being utilized more frequently within the healthcare industry to diagnose and treat a plethora of illnesses. This recent study performed by scientists in the United Kingdom demonstrates that new AI models can be used to accurately diagnose some skin cancers, but that “AI should not be used as a standalone detection tool without the support of a consultant dermatologist,” the press release noted.

“The role of AI in dermatology and the most appropriate pathway are debated,” said Kashini Andrew, MBBS, MSc (above), Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust. “Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool. However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.” Clinical laboratories and dermatopathologists in the United States will want to watch the further development of this AI application. (Photo copyright: LinkedIn.)

How the NHS Scientists Conducted Their Study

Researchers tested their algorithm for almost three years to determine its ability to detect cancerous and pre-cancerous growths. A group of dermatologists and medical photographers entered patient information into their algorithm and trained it how to detect abnormalities. The collected data came from 22,356 patients with suspected skin cancers and included photos of known cancers.

The scientists then repeatedly recalibrated the software to ensure it could distinguish between non-cancerous lesions and potential cancers or malignancies. Dermatologists then reviewed the final data from the algorithm and compared it to diagnoses from health professionals.

“This study has demonstrated how AI is rapidly improving and learning, with the high accuracy directly attributable to improvements in AI training techniques and the quality of data used to train the AI,” said Kashini Andrew, MBBS, MSc, Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust, and co-author of the study, in  EADV press release.

Freeing Up Physician Time

The EADV Congress where the NHS researchers presented their findings took place in October in Berlin. The first model of their AI software was tested in 2021 and that version was able to detect:

  • 85.9% (195 out of 227) of melanoma cases,
  • 83.8% (903 out of 1078) of all skin cancers, and
  • 54.1% (496 out of 917) of pre-cancerous lesions.

After fine-tuning, the latest version of the algorithm was even more promising, with results that included the detection of:

  • 100% (59 out of 59) cases of melanoma,
  • 99.5% (189 out of 190) of all skin cancers, and
  • 92.5% (541 out of 585) pre-cancerous lesions.

“The latest version of the software has saved over 1,000 face-to-face consultations in the secondary care setting between April 2022 and January 2023, freeing up more time for patients that need urgent attention,” Andrew said in the press release.

Still, the researchers admit that AI should not be used as the only detection method for skin cancers.

“We would like to stress that AI should not be used as a standalone tool in skin cancer detection and that AI is not a substitute for consultant dermatologists,” stated Irshad Zaki, B Med Sci (Hons), Consultant Dermatologist at University Hospitals Birmingham NHS Foundation Trust and one of the authors of the study, in the press release.

“The role of AI in dermatology and the most appropriate pathway are debated. Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool,” said Andrew in the press release. “However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.”

Two People in the US Die of Skin Cancer Every Hour

According to the Skin Cancer Foundation, skin cancer is the most common cancer in the United States as well as the rest of the world. More people in the US are diagnosed with skin cancer every year than all other cancers combined.

When detected early, the five-year survival rate for melanoma is 99%, but more than two people in the US die of skin cancer every hour. At least one in five Americans will develop skin cancer by the age of 70 and more than 9,500 people are diagnosed with the disease every day in the US.

The annual cost of treating skin cancers in the United States is estimated at $8.1 billion annually, with approximately $3.3 billion of that amount being for melanoma and the remaining $4.8 billion for non-melanoma skin cancers.

More research is needed before University Hospitals Birmingham’s new AI model can be used clinically in the diagnoses of skin cancers. However, its level of accuracy is unprecedented in AI diagnostics. This is a noteworthy step forward in the field of AI for diagnostic purposes that can be used by clinical laboratories and dermatopathologists.

—JP Schlingman

Related Information:

The App That is 100% Effective at Spotting Some Skin Cancers—as Study Shows Melanoma No Longer the Biggest Killer

AI Software Shows Significant Improvement in Skin Cancer Detection, New Study Shows

Skin Cancer Facts and Statistics

Google DeepMind Says Its New Artificial Intelligence Tool Can Predict Which Genetic Variants Are Likely to Cause Disease

AMA Issues Proposal to Help Circumvent False and Misleading Information When Using Artificial Intelligence in Medicine

UCLA’s Virtual Histology Could Eliminate Need for Invasive Biopsies for Some Skin Conditions and Cancers

University of California San Francisco Study Finds Both High and Low Levels of High-Density Lipoprotein Cholesterol Associated with Increased Dementia Risk

If validated, study findings may result in new biomarkers for clinical laboratory cholesterol tests and for diagnosing dementia

Researchers continue to find new associations between biomarkers commonly tested by clinical laboratories and certain health conditions and diseases. One recent example comes from research conducted by the University of California San Francisco. The UCSF study connected cholesterol biomarkers generally used for managing cardiovascular disease with an increased risk for dementia as well.

The researchers found that both high and low levels of high-density lipoprotein (HDL)—often referred to as “good” cholesterol—was associated with dementia in older adults, according to a news release from the American Academy of Neurology (AAN).

UCSF’s large, longitudinal study incorporated data from 184,367 people in the Kaiser Permanente Northern California health plan. How the findings may alter cholesterol biomarker use in future diagnostics has not been determined.

The researchers published their findings in the journal Neurology titled, “Low- and High-Density Lipoprotein Cholesterol and Dementia Risk over 17 Years of Follow-up among Members of Large Health Care Plan.”

Maria Glymour, ScD

“The elevation in dementia risk with both high and low levels of HDL cholesterol was unexpected, but these increases are small, and their clinical significance is uncertain,” said epidemiologist Maria Glymour, ScD (above), study author and Professor of Epidemiology and Biostatistics at UCSF School of Medicine, in a news release. This is another example of how researchers are associating common biomarkers tested regularly by clinical laboratories with additional health conditions and disease states. (Photo copyright: University of California San Francisco.)

HDL Levels Link to Dementia Risk

The UCSF researchers used cholesterol measurements and health behavior questions as they tracked Kaiser Permanente Northern California health plan members who were at least 55 years old between 2002 and 2007, and who did not have dementia at the time of the study’s launch.

The researchers then followed up with the study participants through December 2020 to find out if they had developed dementia, Medical News Today reported.

“Previous studies on this topic have been inconclusive, and this study is especially informative because of the large number of participants and long follow-up,” said epidemiologist Maria Glymour, ScD, study author and Professor of Epidemiology and Biostatistics at UCSF School of Medicine, in the AAN news release. “This information allowed us to study the links with dementia across the range of cholesterol levels and achieve precise estimates even for people with cholesterol levels that are quite high or quite low.” 

According to HealthDay, UCSF’s study findings included the following:

  • More than 25,000 people developed dementia over about nine years. They were divided into five groups.
  • 53.7 milligrams per deciliter (mg/dL) was the average HDL cholesterol level, amid an optimal range of above 40 mg/dL for men and above 50 mg/dL for women.
  • A 15% rate of dementia was found in participants with HDL of 65 mg/dL or above.
  • A 7% rate of dementia was found in participants with HDL of 11 mg/dL to 41 mg/dL.

“We found a U-shaped relationship between HDL and dementia risk, such that people with either lower or higher HDL had a slightly elevated risk of dementia,” Erin Ferguson, PhD student of Epidemiology at UCSF, the study’s lead study author, told Medical News Today.

What about LDL?

The UCSF researchers found no correlation between low-density lipoprotein (LDL)—often referred to as “bad” cholesterol”—and increased risk for dementia. But the risk did increase slightly when use of statin lipid-lowering medications were included in the analysis.

“Higher LDL was not associated with dementia risk overall, but statin use qualitatively modified the association. Higher LDL was associated with a slightly greater risk of Alzheimer’s disease-related dementia for statin users,” the researchers wrote in Neurology.

“We found no association between LDL cholesterol and dementia risk in the overall study cohort. Our results add to evidence that HDL cholesterol has similarly complex associations with dementia as with heart disease and cancer,” Glymour noted in the AAN news release.

Australian Study also Links High HDL to Dementia

A separate study from Monash University in Melbourne, Victoria, Australia, found that “abnormally high levels” of HDL was also associated with increased risk for dementia, according to a Monash news release.

The Monash study—which was part of the ASPREE (ASPpirin in Reducing Events in the Elderly) trial of people taking daily aspirin—involved 16,703 Australians and 2,411 Americans during the years 2010 to 2014. The researchers found:

  • 850 participants had developed dementia over about six years.
  • A 27% increased risk of dementia among people with HDL above 80 mg/dL and a 42% higher dementia risk for people 75 years and older with high HDL levels.

These findings, Newsweek pointed out, do not necessarily mean that high levels of HDL cause dementia. 

“There might be additional factors that affect both these findings, such as a genetic link that we are currently unaware of,” Andrew Doig, PhD, Professor, Division of Neuroscience at University of Manchester, told Newsweek. Doig was not involved in the in the Monash University research.

Follow-up research could explore the possibility of diagnosing dementia earlier using blood tests and new biomarkers, Newsweek noted.

The Australian researchers published their findings in The Lancet Regional Health-Western Pacific titled, “Association of Plasma High-Density Lipoprotein Cholesterol Level with Risk of Incident Dementia: A Cohort Study of Healthy Older Adults.”

Cholesterol Lab Test Results of Value to Clinical Labs

If further studies validate new biomarkers for testing and diagnosis, a medical laboratory’s longitudinal record of cholesterol test results over many years may be useful in identifying people with an increased risk for dementia.

Clinical pathologists and laboratory managers will want to stay tuned as additional study insights and findings are validated and published. Existing laboratory testing reference ranges may need to be revised as well.

As well, the findings of this UCSF research demonstrate that, in this age of information, there will be plenty of opportunities for clinical lab scientists and pathologists to take their labs’ patient data and combine it with other sets of data. Digital tools like artificial intelligence (AI) and machine learning would then be used to assess that large pool of data and produce clinically actionable insights. In turn, that positions labs to add more value and be paid for that value.

—Donna Marie Pocius

Related Information:

Both High and Low HDL Cholesterol Tied to Increased Risk of Dementia

Low-and High-Density Lipoprotein Cholesterol and Dementia Risk over 17 Years of Follow-up among Members of a Large Health Care Plan

Both High and Low HDL Cholesterol Tied to Slight Increase in Risk of Dementia

How HDL “Good” Cholesterol Might Raise Dementia Risk

HDL vs. LDL Cholesterol

How Levels of “Good” Cholesterol May Increase Dementia Risk

High Levels of “Good Cholesterol” May Be Associated with Dementia Risk, Study Shows

Association of Plasma High-Density Lipoprotein Cholesterol Level with Incident Dementia: A Cohort Study of Healthy Older Adults

Study Claims High Good Cholesterol Levels Linked to Greater Dementia Risk

Cold Spring Harbor Laboratory Researchers Develop Method That Converts Aggressive Cancer Cells into Healthy Cells in Children

If further research confirms these findings, clinical laboratory identification of cancer cells could lead to new treatments for certain childhood cancers

Can cancer cells be changed into normal healthy cells? According to molecular biologists at the Cold Spring Harbor Laboratory (CSHL) in Long Island the answer is, apparently, yes. At least for certain types of cancer. And clinical laboratories and anatomic pathologists may play a key role in identifying these specific cancer cells and then guiding physicians in selecting the most appropriate therapies.

The cancer cells in question are called rhabdomyosarcoma (RMS) and are “particularly aggressive,” according to ScienceAlert. Generally, and most sadly, the cancer primarily affects children below the age of 18. It begins in skeletal muscle, mutates throughout the body, and is often deadly.

“Treatment usually involves chemotherapy, surgery, and radiation procedures. Now, new research by scientists at Cold Spring Harbor Laboratory demonstrates differentiation therapy as a new treatment option for RMS,” Genetic Engineering and Biotechnology News (GEN) reported.

For those young cancer patients, this new research could become a lifesaving therapy as further studies validate the approach, which has been in development for six years.

The CSHL researchers published their findings in the journal Proceedings of the National Academy of Sciences (PNAS) titled, “Myo-Differentiation Reporter Screen Reveals NF-Y as An Activator of PAX3–FOXO1 in Rhabdomyosarcoma.”

Christopher Vakoc, MD, PhD

“Every successful medicine has its origin story,” said Christopher Vakoc, MD, PhD (above), a molecular biologist at Cold Spring Harbor Laboratory, who led the team that develop the method for converting cancer cells into healthy cells. “And research like this is the soil from which new drugs are born.” As these findings are confirmed, it may be that clinical laboratories and anatomic pathologists will be needed to identify the specific cancer cells in patients once treatment is developed. (Photo copyright: Cold Spring Harbor Laboratory.)

Differentiation Therapy

According to an article in the Chinese Journal of Cancer on the National Library of Medicine website, “Differentiation therapy is based on the concept that a neoplasm is a differentiation disorder [aka, differentiation syndrome] or a dedifferentiation disease. In response to the induction of differentiation, tumor cells can revert to normal or nearly normal cells, thereby altering their malignant phenotype and ultimately alleviating the tumor burden or curing the malignant disease without damaging normal cells.”

Vakoc and his team first pursued differentiation therapy to treat Ewing sarcoma, a pediatric cancer that forms in soft tissues or in bone. In January 2023, GEN reported that the researchers had discovered that “Ewing sarcoma could potentially be stopped by developing a drug that blocks the protein known as ETV6.”

“This protein is present in all cells. But when you perturb the protein, most normal cells don’t care,” Vakoc told GEN. “The process by which the sarcoma forms turns this ETV6 molecule—this relatively innocuous, harmless protein that isn’t doing very much—into something that’s now controlling a life-death decision of the tumor cell.”

The researchers discovered that when ETV6 was blocked in lab-grown Ewing sarcoma cells, the cells became normal, healthy cells. “The sarcoma cell reverts back into being a normal cell again,” they told GEN. “The shape of the cell changes. The behavior of the cells changes. A lot of the cells will arrest their growth. It’s really an explosive effect.”

The scientists then turned their attention on Rhabdomyosarcoma to see if they could elicit a similar response.

“In this study, we developed a high-throughput genetic screening method to identify genes that cause rhabdomyosarcoma cells to differentiate into normal muscle. We used this platform to discover the protein NF-Y as an important molecule that contributes to rhabdomyosarcoma biology. CRISPR-based genetic targeting of NF-Y converts rhabdomyosarcoma cells into differentiated muscle, and we reveal the mechanism by which this occurs,” they wrote in PNAS.

“Scientists have successfully induced rhabdomyosarcoma cells to transform into normal, healthy muscle cells. It’s a breakthrough that could see the development of new therapies for the cruel disease, and it could lead to similar breakthroughs for other types of human cancers,” ScienceAlert reported.

“The cells literally turn into muscle,” Vakoc told ScienceAlert. “The tumor loses all cancer attributes. They’re switching from a cell that just wants to make more of itself to cells devoted to contraction. Because all its energy and resources are now devoted to contraction, it can’t go back to this multiplying state,” he added.

Promising New Therapies for Multiple Cancers in Children

Differentiation therapy as a treatment option gained popularity when “scientists noticed that leukemia cells are not fully mature, similar to undifferentiated stem cells that haven’t yet fully developed into a specific cell type. Differentiation therapy forces those cells to continue their development and differentiate into specific mature cell types,” ScienceAlert noted.

Vakoc and his team had previously “effectively reversed the mutation of the cancer cells that emerge in Ewing sarcoma.” It was those promising results from differentiation therapy that inspired the team to push further and attempt success with rhabdomyosarcoma.

Their results are “a key step in the development of differentiation therapy for rhabdomyosarcoma and could accelerate the timeline for which such treatments are expected,” ScienceAlert commented.

Developing New Therapies for Deadly Cancers

Vakoc and his team are considering differentiation therapy’s potential effectiveness for other types of cancer as well. They note that “their technique, now demonstrated on two different types of sarcoma, could be applicable to other sarcomas and cancer types since it gives scientists the tools needed to find how to cause cancer cells to differentiate,” ScienceAlert reported.

“Since many forms of human sarcoma exhibit a defect in cell differentiation, the methodology described here might have broad relevance for the investigation of these tumors,” the researchers wrote in PNAS.

Clinical laboratories and anatomic pathologist play a critical role in identifying many types of cancers. And though any treatment that comes from the Cold Spring Harbor Laboratory research is years away, it illustrates how new insights into the basic dynamics of cancer cells is helping researchers develop effective therapies for attacking those cancers.

—Kristin Althea O’Connor

Related Information:

Aggressive Cancer Cells Transformed into Healthy Cells in Breakthrough

Myo-Differentiation Reporter Screen Reveals NF-Y as An Activator of PAX3–FOXO1 in Rhabdomyosarcoma

Differentiation Therapy: A Promising Strategy for Cancer Treatment

Safer Way to Fight Cancer: Once Rhabdomyosarcoma, Now Muscle

Stopping a Rare Childhood Cancer in Its Tracks

ETV6 Protein Could Be an Important Target for Ewing Sarcoma Treatment

Cancer Cells Turn into Muscle Cells, Potentially Enabling Differentiation Therapy

Novel Ewing Sarcoma Therapeutic Target Uncovered

ETV6 Dependency in Ewing Sarcoma by Antagonism of EWS-FLI1-Mediated Enhancer Activation

Nuclear Transcription Factor Y and Its Roles in Cellular Processes Related to Human Disease

University of Maryland Scientists Image World’s First ‘Vampire Virus’

Research could lead to improvements in gene therapy and antiviral resistance medications while also possibly leading to a new class of clinical laboratory tests

Scientists at the University of Maryland, Baltimore County (UMBC) have discovered what may be the scariest virus of all—the Vampire Virus. It’s a term that may inspire “Walking Dead” level horror in the wake of the COVID-19 pandemic, and though virologists and microbiologists might be tempted to dismiss them as imaginary, they are all too real. Even more apropos to the Dracula saga, the UM scientists found them in a soil sample. Yikes!

Happily, this ghoulish discovery could have positive implications for gene editing, gene therapy, and the development of new antiviral medications, according to The Conversation. In turn, these positive implications may eventually trigger the need to create new diagnostic tests that clinical laboratories can offer to physicians.

The UMBC scientists published their findings in the journal ISME, a publication of the International Society for Microbial Ecology, titled, “Simultaneous Entry as an Adaptation to Virulence in a Novel Satellite-Helper System Infecting Streptomyces Species.”

Vampire-like virus photo

The image above, taken from a University of Maryland news release, shows the satellite virus “latched onto its helper virus.” Discovery of vampire-like viruses that attach at the “neck” of other viruses may lead to important discoveries in the development of gene editing and antiviral therapies. Might clinical laboratories one day collect samples for pharmaceutical developers engaged in combating antiviral drug resistance? (Photo copyright: University of Maryland.)

Spotting a Vampire Virus

According to IFLScience, these tiny vampire viruses were first discovered by undergraduates who believed they were looking at sample contamination when analyzing sequences of bacteriophages from environmental soil samples. But upon repeating the experiment they realized it was no mistake.

In the UMBC news release, bioinformatician Ivan Erill, PhD, Professor of Biological Sciences at the University of Maryland, noted that “some viruses, called satellites, depend not only on their host organism to complete their life cycle, but also on another virus, known as a helper.

“The satellite virus needs the helper either to build its capsid, a protective shell that encloses the virus’ genetic material, or to help it replicate its DNA,” he added. “These viral relationships require the satellite and the helper to be in proximity to each other at least temporarily, but there were no known cases of a satellite actually attaching itself to a helper—until now.”

Although scientists have witnessed viruses working together before, this is the first known instance of a virus directly latching onto another virus’ capsid—rather like a vampire going for the neck.

“When I saw it, I was like, I can’t believe this,” said Tagide deCarvalho, PhD, Assistant Director of Natural and Mathematical Sciences at the University of Maryland and first author of the study, in a UM news release, “No one has ever seen a bacteriophage—or any other virus—attach to another virus.”

Visualizing the tiny viruses was only possible through the use of the transmission electron microscope (TEM) at UMBC’s Keith R. Porter Imaging Facility (KPIF), to which deCarvalho had access.

“Not everyone has a TEM at their disposal. [With the TEM] I’m able to follow up on some of these observations and validate them with imaging. There’s elements of discovery we can only make using the TEM,” said deCarvalho in the UMBC news release.

Using Vampire Viruses to Develop Better Gene Therapies

Spookily, the comparisons to Dracula and his parasitic brethren do not stop with their freeloading tendencies. The researchers found that some viruses without a satellite attached still showed signs of having been leeched onto before. Those viruses had the equivalent of “bite marks” showing evidence of encountering vampiric viruses in the past.

“It’s possible that a lot of the bacteriophages that people thought were contaminated were actually these satellite-helper systems,” said deCarvalho in the ISME paper.

But what does UMBC’s breakthrough mean for the greater scientific and medical community? Do we need to arm host viruses with silver crosses and necklaces of garlic? Jokes aside, this discovery could lead to further development in research of how to genetically alter viruses and deliver therapeutic elements into cells.

According to Healthline, some gene therapy or “gene editing” already involves the use of viruses. Scientists switch out the programming on a virus and trick it into healing, instead of harming the cells it infiltrates. Therefore, UMBC’s discovery could lead to new breakthroughs battling deadly viruses by using their own parasitic tricks to infiltrate other viruses.

Although groundbreaking and extremely interesting, the research is still in early stages. Any developments from this discovery aren’t likely to impact clinical laboratories any time soon. But after the past few years of battling the COVID-19 variants, this exciting discovery could help find new ways to prevent the next pandemic.  

—Ashley Croce

Related Information:

Vampire Viruses Prey on Other Viruses to Replicate Themselves and May Hold the Key to New Antiviral Therapies

Virus Seen Latching onto Another Virus (Like A Tiny Vampire) for First Time

UMBC Team Makes First-Ever Observation of a Virus Attaching to Another Virus

The First Discovered Vampire Virus Hooks Onto other Viruses—Meet the ‘MiniFlayer’

Simultaneous Entry as an Adaptation to Virulence in a Novel Satellite-Helper System infecting Streptomyces Species

Your Guide to Gene Therapy: How It Works and What It Treats

Bizarre First: Viruses Seen ‘Biting’ onto Other Viruses Like Tiny Vampires

AMA Issues Proposal to Help Circumvent False and Misleading Information When Using Artificial Intelligence in Medicine

Pathologists and clinical laboratory managers will want to stay alert to the concerns voiced by tech experts about the need to exercise caution when using generative AI to assist medical diagnoses

Even as many companies push to introduce use of GPT-powered (generative pre-trained transformer) solutions into various healthcare services, both the American Medical Association (AMA) and the World Health Organization (WHO) as well as healthcare professionals urge caution regarding use of AI-powered technologies in the practice of medicine. 

In June, the AMA House of Delegates adopted a proposal introduced by the American Society for Surgery of the Hand (ASSH) and the American Association for Hand Surgery (AAHS) titled, “Regulating Misleading AI Generated Advice to Patients.” The proposal is intended to help protect patients from false and misleading medical information derived from artificial intelligence (AI) tools such as GPTs.

GPTs are an integral part of the framework of a generative artificial intelligence that creates text, images, and other media using generative models. These neural network models can learn the patterns and structure of inputted information and then develop new data that contains similar characteristics.

Through their proposal, the AMA has developed principles and recommendations surrounding the benefits and potentially harmful consequences of relying on AI-generated medical advice and content to advance diagnoses.

Alexander Ding, MD

“We’re trying to look around the corner for our patients to understand the promise and limitations of AI,” said Alexander Ding, MD (above), AMA Trustee and Associate Vice President for Physician Strategy and Medical Affairs at Humana, in a press release. “There is a lot of uncertainty about the direction and regulatory framework for this use of AI that has found its way into the day-to-day practice of medicine.” Clinical laboratory professionals following advances in AI may want to remain informed on the use of generative AI solutions in healthcare. (Photo copyright: American Medical Association.)

Preventing Spread of Mis/Disinformation

GPTs are “a family of neural network models that uses the transformer architecture and is a key advancement in artificial intelligence (AI) powering generative AI applications such as ChatGPT,” according to Amazon Web Services.

In addition to creating human-like text and content, GPTs have the ability to answer questions in a conversational manner. They can analyze language queries and then predict high-quality responses based on their understanding of the language. GPTs can perform this task after being trained with billions of parameters on massive language datasets and then generate long responses, not just the next word in a sequence. 

“AI holds the promise of transforming medicine,” said diagnostic and interventional radiologist Alexander Ding, MD, AMA Trustee and Associate Vice President for Physician Strategy and Medical Affairs at Humana, in an AMA press release.

“We don’t want to be chasing technology. Rather, as scientists, we want to use our expertise to structure guidelines, and guardrails to prevent unintended consequences, such as baking in bias and widening disparities, dissemination of incorrect medical advice, or spread of misinformation or disinformation,” he added.

The AMA plans to work with the federal government and other appropriate organizations to advise policymakers on the optimal ways to use AI in healthcare to protect patients from misleading AI-generated data that may or may not be validated, accurate, or relevant.

Advantages and Risks of AI in Medicine

The AMA’s proposal was prompted by AMA-affiliated organizations that stressed concerns about the lack of regulatory oversight for GPTs. They are encouraging healthcare professionals to educate patients about the advantages and risks of AI in medicine. 

“AI took a huge leap with large language model tool and generative models, so all of the work that has been done up to this point in terms of regulatory and governance frameworks will have to be treated or at least reviewed with this new lens,” Sha Edathumparampil, Corporate Vice President, Digital and Data, Baptist Health South Florida, told Healthcare Brew.

According to the AMA press release, “the current limitations create potential risks for physicians and patients and should be used with appropriate caution at this time. AI-generated fabrications, errors, or inaccuracies can harm patients, and physicians need to be acutely aware of these risks and added liability before they rely on unregulated machine-learning algorithms and tools.”

According to the AMA press release, the organization will propose state and federal regulations for AI tools at next year’s annual meeting in Chicago.

In a July AMA podcast, AMA’s President, Jesse Ehrenfeld, MD, stressed that more must be done through regulation and development to bolster trust in these new technologies.

“There’s a lot of discomfort around the use of these tools among Americans with the idea of AI being used in their own healthcare,” Ehrenfeld said. “There was a 2023 Pew Research Center poll [that said] 60% of Americans would feel uncomfortable if their own healthcare provider relied on AI to do things like diagnose disease or recommend a treatment.”

WHO Issues Cautions about Use of AI in Healthcare

In May, the World Health Organization (WHO) issued a statement advocating for caution when implementing AI-generated large language GPT models into healthcare.

A current example of such a GPT is ChatGPT, a large language-based model (LLM) that enables users to refine and lead conversations towards a desired length, format, style, level of detail and language. Organizations across industries are now utilizing GPT models for Question and Answer bots for customers, text summarization, and content generation and search features. 

“Precipitous adoption of untested systems could lead to errors by healthcare workers, cause harm to patients, erode trust in AI, and thereby undermine (or delay) the potential long-term benefits and uses of such technologies around the world,” commented WHO in the statement.

WHO’s concerns regarding the need for prudence and oversight in the use of AI technologies include:

  • Data used to train AI may be biased, which could pose risks to health, equity, and inclusiveness.
  • LLMs generate responses that can appear authoritative and plausible, but which may be completely incorrect or contain serious errors.
  • LLMs may be trained on data for which consent may not have been given.
  • LLMs may not be able to protect sensitive data that is provided to an application to generate a response.
  • LLMs can be misused to generate and disseminate highly convincing disinformation in the form of text, audio, or video that may be difficult for people to differentiate from reliable health content.

Tech Experts Recommended Caution

Generative AI will continue to evolve. Therefore, clinical laboratory professionals may want to keep a keen eye on advances in AI technology and GPTs in healthcare diagnosis.

“While generative AI holds tremendous potential to transform various industries, it also presents significant challenges and risks that should not be ignored,” wrote Edathumparampil in an article he penned for CXOTECH Magazine. “With the right strategy and approach, generative AI can be a powerful tool for innovation and differentiation, helping businesses to stay ahead of the competition and better serve their customers.”

GPT’s may eventually be a boon to healthcare providers, including clinical laboratories, and pathology groups. But for the moment, caution is recommended.

JP Schlingman

Related Information:

AMA Adopts Proposal to Protect Patients from False and Misleading AI-generated Medical Advice

Regulating Misleading AI Generated Advice to Patients

AMA to Develop Recommendations for Augmented Intelligence

What is GPT?

60% of Americans Would Be Uncomfortable with Provider Relying on AI in Their Own Health Care

Navigating the Risks of Generative AI: A Guide for Businesses

Contributed: Top 10 Use Cases for AI in Healthcare

Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now

ChatGPT, AI in Healthcare and the future of Medicine with AMA President Jesse Ehrenfeld, MD, MPH

What is Generative AI? Everything You Need to Know

WHO Calls for Safe and Ethical AI for Health

GPT-3

;